code: def test_fc_layer(
      input_size: int,
      output_size: int,
      activation: str,
      dropout: float,
      batch_size: int,
      norm: Optional[str],
  ):
      set_random_seed(RANDOM_SEED)  # make repeatable
      fc_layer = FCLayer(
          input_size=input_size,
          output_size=output_size,
          activation=activation,
          dropout=dropout,
          norm=norm,
      ).to(DEVICE)
      input_tensor = torch.randn(batch_size, input_size, device=DEVICE)
      output_tensor = fc_layer(input_tensor)
      assert output_tensor.shape[1:] == fc_layer.output_shape
  
file_path: ludwig/tests/ludwig/modules/test_fully_connected_modules.py
imports: from typing import List, Optional
  import numpy
  import pytest
  import torch
  from ludwig.modules.fully_connected_modules import FCLayer, FCStack
  from ludwig.utils.misc_utils import set_random_seed
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 1 for calling a global function with example usage: test_fc_layer(input_size, output_size, activation, dropout, batch_size, norm) without return types